EMT Project Course (Projektpraktikum)

Project Course / Projektpraktikum
4 SWS
Module: TUMonline Course Registration

SS 2026S Nr: 0000002995
Language of Instruction: English

Description

Participants of the course gain hands-on experience in research projects related to Energy Management.
Possible topics include but not limited to:
- EMS and distributed energy systems development
- Grid simulation
- Energy system optimizations
- Energy forecasting
- Energy Data curation and analytics
Both individual and group work is possible.
A kickoff session will be conducted to introduce the course and topics. Afterwards, students can apply for topics by sending the top three topics and the current transcript of records to paul.loer@tum.de.
Regular follow-up meetings with the participants and supervisor are held to aid the participants with the project tasks.
All presentations and reports in English. Advisor↔participant communication language may vary.

Deliverables, important dates, and current topics listed below.

Deliverables

At the end of the semester, participants will have to submit:

a scientific paper (~7 pages per student, submitted as the final report).
a conference-style final presentation, including demo, or proof of concept.

Both of these must be submitted on time and any late work may not be accepted.

Topic Allocation

The final topic application will be done by the date indicated below (after the Kickoff meeting). After this point, students will receive a Fixed Place confirmation in TUM online.
Note: Students looking to secure a place early on may reach out to supervisors, who can preliminarily accept students at their discretion.

Topic preference submissions should be sent to paul.loer@tum.de, and include the following information:

Name
Course of Study
CV
Transcript of Records
Top 3 Preferences (in order)

Please prepend the email subject with “[EMT Projektpraktikum SS2026]”, for example: “[EMT Projektpraktikum SS2026] Topic Preference”.

For specific questions about a topic, please contact the indicated supervisor.

Important Dates (Summer Semester 2026)

	Date	Time	Place
Registration Period	14.04.26	23:59	TUMOnline
Kickoff Meeting	15.04.26	16:30 - 17:30	room N3815
Topic preferences submission	17.04.26	23:59	email
Topic allocation	20.04.26	19:00	email
Final Report and presentation due	14.07.26	23:59	moodle
Final Presentation	15.07.26	16:30-17:30	room N3815

Kickoff Meeting

During the Kickoff Meeting, supervisors will introduce the course and the available topics. Students will also have a chance to ask questions and directly connect with supervisors.
In-person attendance is recommended.

Topics

If you have any more questions about a topic, please contact the supervisors directly. For topic preference submissions, contact paul.loer@tum.de.

Acronym	Topic description	Required skills	Number of Students	Contact	Time Topic Added
ESPARX	Using e-SparX for Developing Forecasting Models Research method: Prototyping Research questions: How can e-SparX be used to develop forecasting models and make them transparent and sharable? How to extend and improve e-SparX to make it more useful and usable? Possible approach: Select 1-2 energy forecasting tasks (e.g., load or solar forecasting) based on literature Understand current e-SparX implementation Develop efficient Python code to implement several forecasting models Share the complete ML pipelines using e-SparX Propose new e-SparX features to improve its usability Optionally implement new e-SparX features in separate branch of codebase	Python programming or motivation to learn Python for machine learning Interest in software engineering	2-4	Christoph Goebel christoph.goebel@tum.de
EDA	Exploratory Data Analysis for Open Energy Data This topic will contribute to our professorship's effort to make open energy data easier to find and accessible. Find out more here: TUM-EMT Open Data Collection Research method: Data analysis Research questions: How can we explore and understand open energy data efficiently? How can we visualize open energy data clearly and comprehensively? How can exploratory data analysis for open energy data be standardized and automated? Possible approach: Research and gather high-quality open energy datasets Play with and understand the dataset at hand Program comprehensive and visually appealing Jupyter notebooks to demonstrate and explain the data to energy researchers Provide first data processing and data analysis Resources: TUM-EMT Open Data Collection https://gitlab.lrz.de/energy-management-technologies-public/open-data-collection https://www.dataquest.io/blog/jupyter-notebook-tutorial/	Python programming or motivation to learn Python for data analysis Interest in data exploration	2-4	Manuel Katholnigg manuel.katholnigg@tum.de
ANN_OPF	Power Grids ANN-based Optimal Power Flow Research method: Prototyping Research question: What model design is suitable for improving ANNs optimal power flow predictions? What training approaches are suitable for maintain predictions feasibility and optimality? Possible approach: Understand state-of-the-art based on literature Implement ANN model for optimal power-flow prediction Benchmark results against SOTA ANNs	Programming skills in Python	1-2	Arbel Yaniv arbel.yaniv@tum.de	2025-09
EWF	High resolution extreme weather event forecasting Research method: Prototyping Research question: How well can AI models forecast extreme weather events? Which AI model performs better for extreme weather events forecast? Possible approach: Understand state-of-the-art based on literature Leverage existing state-of-the-art methods on extreme weather event data Benchmark against existing numerical and ML approaches Resources: [1] Ran, Nian, et al. "Hr-extreme: A high-resolution dataset for extreme weather forecasting." arXiv preprint arXiv:2409.18885 (2024).	Python programming skills Machine learning	1-2	Arbel Yaniv arbel.yaniv@tum.de	03/2025

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